I also started building similar tools in early 2025 and have built around 80 of them so far: https://tools.o14.ai/. Recently, I built a data viewer app (https://tools.o14.ai/excel-qa-review.html) where I can upload an Excel or CSV file containing chat queries and answers for quick manual review.
Reviewing data in Excel is painful, especially when answers are in HTML or Markdown, because you don’t get proper rendering. Building small, custom tools that reduce the friction of reviewing data makes life much easier and more pleasant. These days, I use Claude Code for Web to build most of these apps, and they are deployed on Vercel.
Not sure why they used React for a CLI. The code in the repo feels like it was written by an LLM—too many inline comments. Interestingly, their agent's system prompt mentions removing inline comments https://github.com/openai/codex/blob/main/codex-cli/src/util....
> - Remove all inline comments you added as much as possible, even if they look normal. Check using \`git diff\`. Inline comments must be generally avoided, unless active maintainers of the repo, after long careful study of the code and the issue, will still misinterpret the code without the comments.
Google AI Overview incorrectly identified the day for a given date due to a timezone conversion issue, likely using PST instead of IST. ChatGPT and Perplexity provided more accurate and detailed responses.
I am building https://www.videocrawl.dev/, an AI companion web application that enhances the video-watching and learning experience. Since I primarily learn from videos, I built this to improve my learning workflow. It is free to use.
It offers standard features like chatting with videos, summarization, FAQs, note-taking, and extracting sources mentioned in the video. We're taking it a step further by extracting relevant information from video frames and making it easily accessible.
We built Videocrawl [1] to enhance the learning and watching experience using LLMs. It handles the usual tasks like clean transcript extraction, summarization, and chat-based interaction with videos. However, we go a step further by analyzing frames to extract code snippets, references, sources, and more.
You can try it out by watching a video on Videocrawl, such as the OpenAI Agent video, by following this link [2]. LLMs have the potential to significantly improve how we learn from and engage with videos.
Mistral OCR made multiple mistakes in extracting this [1] document. It is a two-page-long PDF in Arabic from the Saudi Central Bank. The following errors were observed:
- Referenced Vision 2030 as Vision 2.0.
- Failed to extract the table; instead, it hallucinated and extracted the text in a different format.
- Failed to extract the number and date of the circular.
I tested the same document with ChatGPT, Claude, Grok, and Gemini. Only Claude 3.7 extracted the complete document, while all others failed badly. You can read my analysis here [2].
I asked Claude 3.7 Sonnet to generate an SVG illustration of Maha Kumbh. The generated SVG includes a Shivling (https://en.wikipedia.org/wiki/Lingam) and also depicts Naga Sadhus well. Both Grok 3 and OpenAI o3 failed miserably.
I’m building Videocrawl https://www.videocrawl.dev/ a platform designed to make video content more accessible and actionable. It offers two key features:
1. API for Clean Transcripts – Extract structured transcripts with references, code snippets, and images.
2. AI Video Assistant – Interact with videos using AI-powered tools for summarization, Q&A, and more.
Would love to hear your thoughts! You can reach out to me at [email protected]
I don't know, but I found the recording uninspiring. There was nothing new for me. We've all seen reasoning models by now—we know they work well for certain use cases. We've also seen "Deep Researchers," so nothing new there either.
No matter what people say, they're all just copying OpenAI. I'm not a huge fan of OpenAI, but I think they're still the ones showing what can be done. Yes, xAI might have taken less time because of their huge cluster, but it’s not inspiring to me. Also, the dark room setup was depressing.
Have people tried using R1 for some real-world use cases? I attempted to use the 7b Ollama variant for my UI generation [1] and Gitlab Postgres Schema Analysis [2] tasks, but the results were not satisfactory.
- UI Generation: The generated UI failed to function due to errors in the JavaScript, and the overall user experience was poor.
- Gitlab Postgres Schema Analysis: It identified only a few design patterns.
I am not sure if these are suitable tasks for R1. I will try larger variant as well.
This is the same approach we took when we added LLM capability to a low code tool Appian. LLM helped us generate the Appian workflow configuration file, user reviews it and make changes if required, and then finally publishes it.
Reviewing data in Excel is painful, especially when answers are in HTML or Markdown, because you don’t get proper rendering. Building small, custom tools that reduce the friction of reviewing data makes life much easier and more pleasant. These days, I use Claude Code for Web to build most of these apps, and they are deployed on Vercel.